In recent years, we’ve all felt the seismic shift caused by technologies like Chat-GPT, transforming not just how we work but the very nature of our roles. Among these evolving careers, project management stands at the crossroads of change, significantly impacted by the rapid introduction of artificial intelligence (AI). But what does this actually mean for project managers (PMs)? Amidst the AI buzzword and chorus such as “change is coming,” specific, actionable insights on how to adapt remain elusive, leaving many of us floating in a sea of uncertainty. How do we steer through this fog of rapid technological advancement? Are we ready for the changes AI promises to bring? And how does it feel to face this unfamiliar territory, where the pressure for us to stay ahead is substantial, yet the path forward seems unclear?
Let’s start by demystifying the intersection of project management and AI, breaking down the facade to reveal the concrete impacts and opportunities it offers. Three intersection points emerge.
At the heart of this intersection is the direct application of AI in the day-to-day tasks of a project manager. But when exactly should we consider leveraging AI? Imagine enhancing your project’s planning phase, streamlining budget control, or even optimizing communication—all through AI. The key is to experiment, be curious, and actively seek out how these tools can elevate your work. Don’t wait to be told by another PM; dive in and explore the possibilities yourself.
The consensus among PMs highlights several areas ripe for AI integration:
If you haven’t yet experimented with real-life project situations, go ahead. If you are still unsure where to start from, feel free to check our guidelines, showing some practical use cases.
But more is coming, dear PMs. Picture this: having your own AI PM assistant. Imagine how, through a chat interface, it reaches out to all your team members to gather specific project status updates. This assistant isn’t just collecting data; it’s engaging with your team, asking the right questions to ensure it gathers all necessary information. It then analyzes responses, providing a consolidated summary and pinpointing potential issues with spot-on recommendations. This will be a tangible change to our profession, wouldn’t you agree?
A significant portion of corporate projects in the last decade focused on software rollouts, from ERPs to entire corporate systems. Today, we’re standing in front of an even larger wave: AI implementation. Common AI implementation projects now include everything from developing sophisticated chatbots that improve customer service to implementing machine learning models that predict customer behavior, such as churn rates.
The system rollouts from the last years included relatively standardized processes with typical phases like system setup, training, user testing, and cutover. AI implementation projects, on the other hand, introduce a different project stream, related to the complex decision around the right AI / ML technology and model to be used.
This “discovery” phase becomes a pivotal starting point. It involves the data science team undertaking a mission of exploration and innovation:
Fig.1: AI model development – phases.
Unlike traditional software deployments, such projects will require a more integrated approach, involving both product and model development streams. Project managers need to be more involved than ever, working closely with AI engineers and data scientists to steer these initiatives successfully. And, as said earlier, we should expect an abundance of such implementation projects in the near future.
As AI becomes more integrated into businesses, consider the human resources department, where an AI Agent could be introduced to automate the screening and initial interviewing process for new candidates—a task currently performed by people. Or another agent that gathers information from a few separate systems and folders in our company to provide you with a complex report, that you would take hours to compile for your project. The landscape in which we operate will fundamentally change. This shift will affect the organizational process assets (OPAs) and enterprise environmental factors (EEFs) that PMs rely on. OPAs—those plans, policies, and knowledge bases specific to an organization—may include AI-driven tools and processes. EEFs, which encompass external and internal policies and conditions in the work environment affecting project management, will also reflect an AI-integrated world. Even projects not directly related to AI will still operate within an AI-influenced environment, changing also the project management practices.
The integration of AI in project management is not just inevitable; it’s already happening. While the introduction of AI into project management might seem daunting, it doesn’t spell doom. On the contrary, it offers a chance to leap forward, to innovate, and improve the way projects are managed. However, the pace at which you adapt could very well determine your success. Don’t let your PM skills stagnate; the future waits for no one. Embrace this change with an open mind and a proactive stance, and you’ll find yourself not just keeping up but leading the way in the project management arena of tomorrow.
